Continual Semantic Segmentation [1]} is the combination of continual learning and semantic segmentation [2]}, [3]}, [4]}, [5]}. This setting has been refined into two scenarios: Disjoint and Overlapped [6]}, [7]}. In both scenarios, classes are added incrementally and no storing of previous images is allowed. In the former, an image of task \(t\)  may contain either old or current classes. In the latter, it can also contain future yet-to-be-seen classes. In both scenarios, the ground-truth segmentation masks are partially labeled: current classes are labeled but all others classes are assigned a background label. The goal of this setting is therefore to learn continuously new classes, while not forgetting old classes that may be present in the current images but unlabeled.
